This has been mentioned before in #254, but I want to elaborate on our difficulties.
This type of hardcoded dtypes makes it extremely hard to move our programs to use float64.
For example, if we use tf.keras.backend.set_floatx('float64') anywhere, we get errors within tensorflow_ranking due to conflicting dtypes.
Will the global floating point policy (tf.keras.mixed_precision.set_global_policy and tf.keras.backend.floatx) be supported?
If the official stance on the global policy is to ignore it, can it be documented?
The issue of float precision affects many computations in
tensorflow_ranking, such as https://github.com/tensorflow/ranking/blob/a928e2b1930a1ebcae2c509e3f6ca95941fd1e49/tensorflow_ranking/python/metrics_impl.py#L603-L628This has been mentioned before in #254, but I want to elaborate on our difficulties. This type of hardcoded dtypes makes it extremely hard to move our programs to use
float64. For example, if we usetf.keras.backend.set_floatx('float64')anywhere, we get errors withintensorflow_rankingdue to conflicting dtypes.Will the global floating point policy (
tf.keras.mixed_precision.set_global_policyandtf.keras.backend.floatx) be supported? If the official stance on the global policy is to ignore it, can it be documented?